{"ID":2860663,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00004","arxiv_id":"2511.00004","title":"Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment","abstract":"Natural disaster assessment relies on accurate and rapid access to information, with social media emerging as a valuable real-time source. However, existing datasets suffer from class imbalance and limited samples, making effective model development a challenging task. This paper explores augmentation techniques to address these issues on the CrisisMMD multimodal dataset. For visual data, we apply diffusion-based methods, namely Real Guidance and DiffuseMix. For text data, we explore back-translation, paraphrasing with transformers, and image caption-based augmentation. We evaluated these across unimodal, multimodal, and multi-view learning setups. Results show that selected augmentations improve classification performance, particularly for underrepresented classes, while multi-view learning introduces potential but requires further refinement. This study highlights effective augmentation strategies for building more robust disaster assessment systems.","short_abstract":"Natural disaster assessment relies on accurate and rapid access to information, with social media emerging as a valuable real-time source. However, existing datasets suffer from class imbalance and limited samples, making effective model development a challenging task. This paper explores augmentation techniques to add...","url_abs":"https://arxiv.org/abs/2511.00004","url_pdf":"https://arxiv.org/pdf/2511.00004v1","authors":"[\"Adrian-Dinu Urse\",\"Dumitru-Clementin Cercel\",\"Florin Pop\"]","published":"2025-10-04T18:51:54Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\",\"cs.CL\",\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}